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Diffstat (limited to 'third_party/libwebrtc/common_audio/vad/vad_core.c')
| -rw-r--r-- | third_party/libwebrtc/common_audio/vad/vad_core.c | 685 |
1 files changed, 685 insertions, 0 deletions
diff --git a/third_party/libwebrtc/common_audio/vad/vad_core.c b/third_party/libwebrtc/common_audio/vad/vad_core.c new file mode 100644 index 0000000000..0872449a7c --- /dev/null +++ b/third_party/libwebrtc/common_audio/vad/vad_core.c @@ -0,0 +1,685 @@ +/* + * Copyright (c) 2012 The WebRTC project authors. All Rights Reserved. + * + * Use of this source code is governed by a BSD-style license + * that can be found in the LICENSE file in the root of the source + * tree. An additional intellectual property rights grant can be found + * in the file PATENTS. All contributing project authors may + * be found in the AUTHORS file in the root of the source tree. + */ + +#include "common_audio/vad/vad_core.h" + +#include "rtc_base/sanitizer.h" +#include "common_audio/signal_processing/include/signal_processing_library.h" +#include "common_audio/vad/vad_filterbank.h" +#include "common_audio/vad/vad_gmm.h" +#include "common_audio/vad/vad_sp.h" + +// Spectrum Weighting +static const int16_t kSpectrumWeight[kNumChannels] = { 6, 8, 10, 12, 14, 16 }; +static const int16_t kNoiseUpdateConst = 655; // Q15 +static const int16_t kSpeechUpdateConst = 6554; // Q15 +static const int16_t kBackEta = 154; // Q8 +// Minimum difference between the two models, Q5 +static const int16_t kMinimumDifference[kNumChannels] = { + 544, 544, 576, 576, 576, 576 }; +// Upper limit of mean value for speech model, Q7 +static const int16_t kMaximumSpeech[kNumChannels] = { + 11392, 11392, 11520, 11520, 11520, 11520 }; +// Minimum value for mean value +static const int16_t kMinimumMean[kNumGaussians] = { 640, 768 }; +// Upper limit of mean value for noise model, Q7 +static const int16_t kMaximumNoise[kNumChannels] = { + 9216, 9088, 8960, 8832, 8704, 8576 }; +// Start values for the Gaussian models, Q7 +// Weights for the two Gaussians for the six channels (noise) +static const int16_t kNoiseDataWeights[kTableSize] = { + 34, 62, 72, 66, 53, 25, 94, 66, 56, 62, 75, 103 }; +// Weights for the two Gaussians for the six channels (speech) +static const int16_t kSpeechDataWeights[kTableSize] = { + 48, 82, 45, 87, 50, 47, 80, 46, 83, 41, 78, 81 }; +// Means for the two Gaussians for the six channels (noise) +static const int16_t kNoiseDataMeans[kTableSize] = { + 6738, 4892, 7065, 6715, 6771, 3369, 7646, 3863, 7820, 7266, 5020, 4362 }; +// Means for the two Gaussians for the six channels (speech) +static const int16_t kSpeechDataMeans[kTableSize] = { + 8306, 10085, 10078, 11823, 11843, 6309, 9473, 9571, 10879, 7581, 8180, 7483 +}; +// Stds for the two Gaussians for the six channels (noise) +static const int16_t kNoiseDataStds[kTableSize] = { + 378, 1064, 493, 582, 688, 593, 474, 697, 475, 688, 421, 455 }; +// Stds for the two Gaussians for the six channels (speech) +static const int16_t kSpeechDataStds[kTableSize] = { + 555, 505, 567, 524, 585, 1231, 509, 828, 492, 1540, 1079, 850 }; + +// Constants used in GmmProbability(). +// +// Maximum number of counted speech (VAD = 1) frames in a row. +static const int16_t kMaxSpeechFrames = 6; +// Minimum standard deviation for both speech and noise. +static const int16_t kMinStd = 384; + +// Constants in WebRtcVad_InitCore(). +// Default aggressiveness mode. +static const short kDefaultMode = 0; +static const int kInitCheck = 42; + +// Constants used in WebRtcVad_set_mode_core(). +// +// Thresholds for different frame lengths (10 ms, 20 ms and 30 ms). +// +// Mode 0, Quality. +static const int16_t kOverHangMax1Q[3] = { 8, 4, 3 }; +static const int16_t kOverHangMax2Q[3] = { 14, 7, 5 }; +static const int16_t kLocalThresholdQ[3] = { 24, 21, 24 }; +static const int16_t kGlobalThresholdQ[3] = { 57, 48, 57 }; +// Mode 1, Low bitrate. +static const int16_t kOverHangMax1LBR[3] = { 8, 4, 3 }; +static const int16_t kOverHangMax2LBR[3] = { 14, 7, 5 }; +static const int16_t kLocalThresholdLBR[3] = { 37, 32, 37 }; +static const int16_t kGlobalThresholdLBR[3] = { 100, 80, 100 }; +// Mode 2, Aggressive. +static const int16_t kOverHangMax1AGG[3] = { 6, 3, 2 }; +static const int16_t kOverHangMax2AGG[3] = { 9, 5, 3 }; +static const int16_t kLocalThresholdAGG[3] = { 82, 78, 82 }; +static const int16_t kGlobalThresholdAGG[3] = { 285, 260, 285 }; +// Mode 3, Very aggressive. +static const int16_t kOverHangMax1VAG[3] = { 6, 3, 2 }; +static const int16_t kOverHangMax2VAG[3] = { 9, 5, 3 }; +static const int16_t kLocalThresholdVAG[3] = { 94, 94, 94 }; +static const int16_t kGlobalThresholdVAG[3] = { 1100, 1050, 1100 }; + +// Calculates the weighted average w.r.t. number of Gaussians. The `data` are +// updated with an `offset` before averaging. +// +// - data [i/o] : Data to average. +// - offset [i] : An offset added to `data`. +// - weights [i] : Weights used for averaging. +// +// returns : The weighted average. +static int32_t WeightedAverage(int16_t* data, int16_t offset, + const int16_t* weights) { + int k; + int32_t weighted_average = 0; + + for (k = 0; k < kNumGaussians; k++) { + data[k * kNumChannels] += offset; + weighted_average += data[k * kNumChannels] * weights[k * kNumChannels]; + } + return weighted_average; +} + +// An s16 x s32 -> s32 multiplication that's allowed to overflow. (It's still +// undefined behavior, so not a good idea; this just makes UBSan ignore the +// violation, so that our old code can continue to do what it's always been +// doing.) +static inline int32_t RTC_NO_SANITIZE("signed-integer-overflow") + OverflowingMulS16ByS32ToS32(int16_t a, int32_t b) { + return a * b; +} + +// Calculates the probabilities for both speech and background noise using +// Gaussian Mixture Models (GMM). A hypothesis-test is performed to decide which +// type of signal is most probable. +// +// - self [i/o] : Pointer to VAD instance +// - features [i] : Feature vector of length `kNumChannels` +// = log10(energy in frequency band) +// - total_power [i] : Total power in audio frame. +// - frame_length [i] : Number of input samples +// +// - returns : the VAD decision (0 - noise, 1 - speech). +static int16_t GmmProbability(VadInstT* self, int16_t* features, + int16_t total_power, size_t frame_length) { + int channel, k; + int16_t feature_minimum; + int16_t h0, h1; + int16_t log_likelihood_ratio; + int16_t vadflag = 0; + int16_t shifts_h0, shifts_h1; + int16_t tmp_s16, tmp1_s16, tmp2_s16; + int16_t diff; + int gaussian; + int16_t nmk, nmk2, nmk3, smk, smk2, nsk, ssk; + int16_t delt, ndelt; + int16_t maxspe, maxmu; + int16_t deltaN[kTableSize], deltaS[kTableSize]; + int16_t ngprvec[kTableSize] = { 0 }; // Conditional probability = 0. + int16_t sgprvec[kTableSize] = { 0 }; // Conditional probability = 0. + int32_t h0_test, h1_test; + int32_t tmp1_s32, tmp2_s32; + int32_t sum_log_likelihood_ratios = 0; + int32_t noise_global_mean, speech_global_mean; + int32_t noise_probability[kNumGaussians], speech_probability[kNumGaussians]; + int16_t overhead1, overhead2, individualTest, totalTest; + + // Set various thresholds based on frame lengths (80, 160 or 240 samples). + if (frame_length == 80) { + overhead1 = self->over_hang_max_1[0]; + overhead2 = self->over_hang_max_2[0]; + individualTest = self->individual[0]; + totalTest = self->total[0]; + } else if (frame_length == 160) { + overhead1 = self->over_hang_max_1[1]; + overhead2 = self->over_hang_max_2[1]; + individualTest = self->individual[1]; + totalTest = self->total[1]; + } else { + overhead1 = self->over_hang_max_1[2]; + overhead2 = self->over_hang_max_2[2]; + individualTest = self->individual[2]; + totalTest = self->total[2]; + } + + if (total_power > kMinEnergy) { + // The signal power of current frame is large enough for processing. The + // processing consists of two parts: + // 1) Calculating the likelihood of speech and thereby a VAD decision. + // 2) Updating the underlying model, w.r.t., the decision made. + + // The detection scheme is an LRT with hypothesis + // H0: Noise + // H1: Speech + // + // We combine a global LRT with local tests, for each frequency sub-band, + // here defined as `channel`. + for (channel = 0; channel < kNumChannels; channel++) { + // For each channel we model the probability with a GMM consisting of + // `kNumGaussians`, with different means and standard deviations depending + // on H0 or H1. + h0_test = 0; + h1_test = 0; + for (k = 0; k < kNumGaussians; k++) { + gaussian = channel + k * kNumChannels; + // Probability under H0, that is, probability of frame being noise. + // Value given in Q27 = Q7 * Q20. + tmp1_s32 = WebRtcVad_GaussianProbability(features[channel], + self->noise_means[gaussian], + self->noise_stds[gaussian], + &deltaN[gaussian]); + noise_probability[k] = kNoiseDataWeights[gaussian] * tmp1_s32; + h0_test += noise_probability[k]; // Q27 + + // Probability under H1, that is, probability of frame being speech. + // Value given in Q27 = Q7 * Q20. + tmp1_s32 = WebRtcVad_GaussianProbability(features[channel], + self->speech_means[gaussian], + self->speech_stds[gaussian], + &deltaS[gaussian]); + speech_probability[k] = kSpeechDataWeights[gaussian] * tmp1_s32; + h1_test += speech_probability[k]; // Q27 + } + + // Calculate the log likelihood ratio: log2(Pr{X|H1} / Pr{X|H1}). + // Approximation: + // log2(Pr{X|H1} / Pr{X|H1}) = log2(Pr{X|H1}*2^Q) - log2(Pr{X|H1}*2^Q) + // = log2(h1_test) - log2(h0_test) + // = log2(2^(31-shifts_h1)*(1+b1)) + // - log2(2^(31-shifts_h0)*(1+b0)) + // = shifts_h0 - shifts_h1 + // + log2(1+b1) - log2(1+b0) + // ~= shifts_h0 - shifts_h1 + // + // Note that b0 and b1 are values less than 1, hence, 0 <= log2(1+b0) < 1. + // Further, b0 and b1 are independent and on the average the two terms + // cancel. + shifts_h0 = WebRtcSpl_NormW32(h0_test); + shifts_h1 = WebRtcSpl_NormW32(h1_test); + if (h0_test == 0) { + shifts_h0 = 31; + } + if (h1_test == 0) { + shifts_h1 = 31; + } + log_likelihood_ratio = shifts_h0 - shifts_h1; + + // Update `sum_log_likelihood_ratios` with spectrum weighting. This is + // used for the global VAD decision. + sum_log_likelihood_ratios += + (int32_t) (log_likelihood_ratio * kSpectrumWeight[channel]); + + // Local VAD decision. + if ((log_likelihood_ratio * 4) > individualTest) { + vadflag = 1; + } + + // TODO(bjornv): The conditional probabilities below are applied on the + // hard coded number of Gaussians set to two. Find a way to generalize. + // Calculate local noise probabilities used later when updating the GMM. + h0 = (int16_t) (h0_test >> 12); // Q15 + if (h0 > 0) { + // High probability of noise. Assign conditional probabilities for each + // Gaussian in the GMM. + tmp1_s32 = (noise_probability[0] & 0xFFFFF000) << 2; // Q29 + ngprvec[channel] = (int16_t) WebRtcSpl_DivW32W16(tmp1_s32, h0); // Q14 + ngprvec[channel + kNumChannels] = 16384 - ngprvec[channel]; + } else { + // Low noise probability. Assign conditional probability 1 to the first + // Gaussian and 0 to the rest (which is already set at initialization). + ngprvec[channel] = 16384; + } + + // Calculate local speech probabilities used later when updating the GMM. + h1 = (int16_t) (h1_test >> 12); // Q15 + if (h1 > 0) { + // High probability of speech. Assign conditional probabilities for each + // Gaussian in the GMM. Otherwise use the initialized values, i.e., 0. + tmp1_s32 = (speech_probability[0] & 0xFFFFF000) << 2; // Q29 + sgprvec[channel] = (int16_t) WebRtcSpl_DivW32W16(tmp1_s32, h1); // Q14 + sgprvec[channel + kNumChannels] = 16384 - sgprvec[channel]; + } + } + + // Make a global VAD decision. + vadflag |= (sum_log_likelihood_ratios >= totalTest); + + // Update the model parameters. + maxspe = 12800; + for (channel = 0; channel < kNumChannels; channel++) { + + // Get minimum value in past which is used for long term correction in Q4. + feature_minimum = WebRtcVad_FindMinimum(self, features[channel], channel); + + // Compute the "global" mean, that is the sum of the two means weighted. + noise_global_mean = WeightedAverage(&self->noise_means[channel], 0, + &kNoiseDataWeights[channel]); + tmp1_s16 = (int16_t) (noise_global_mean >> 6); // Q8 + + for (k = 0; k < kNumGaussians; k++) { + gaussian = channel + k * kNumChannels; + + nmk = self->noise_means[gaussian]; + smk = self->speech_means[gaussian]; + nsk = self->noise_stds[gaussian]; + ssk = self->speech_stds[gaussian]; + + // Update noise mean vector if the frame consists of noise only. + nmk2 = nmk; + if (!vadflag) { + // deltaN = (x-mu)/sigma^2 + // ngprvec[k] = `noise_probability[k]` / + // (`noise_probability[0]` + `noise_probability[1]`) + + // (Q14 * Q11 >> 11) = Q14. + delt = (int16_t)((ngprvec[gaussian] * deltaN[gaussian]) >> 11); + // Q7 + (Q14 * Q15 >> 22) = Q7. + nmk2 = nmk + (int16_t)((delt * kNoiseUpdateConst) >> 22); + } + + // Long term correction of the noise mean. + // Q8 - Q8 = Q8. + ndelt = (feature_minimum << 4) - tmp1_s16; + // Q7 + (Q8 * Q8) >> 9 = Q7. + nmk3 = nmk2 + (int16_t)((ndelt * kBackEta) >> 9); + + // Control that the noise mean does not drift to much. + tmp_s16 = (int16_t) ((k + 5) << 7); + if (nmk3 < tmp_s16) { + nmk3 = tmp_s16; + } + tmp_s16 = (int16_t) ((72 + k - channel) << 7); + if (nmk3 > tmp_s16) { + nmk3 = tmp_s16; + } + self->noise_means[gaussian] = nmk3; + + if (vadflag) { + // Update speech mean vector: + // `deltaS` = (x-mu)/sigma^2 + // sgprvec[k] = `speech_probability[k]` / + // (`speech_probability[0]` + `speech_probability[1]`) + + // (Q14 * Q11) >> 11 = Q14. + delt = (int16_t)((sgprvec[gaussian] * deltaS[gaussian]) >> 11); + // Q14 * Q15 >> 21 = Q8. + tmp_s16 = (int16_t)((delt * kSpeechUpdateConst) >> 21); + // Q7 + (Q8 >> 1) = Q7. With rounding. + smk2 = smk + ((tmp_s16 + 1) >> 1); + + // Control that the speech mean does not drift to much. + maxmu = maxspe + 640; + if (smk2 < kMinimumMean[k]) { + smk2 = kMinimumMean[k]; + } + if (smk2 > maxmu) { + smk2 = maxmu; + } + self->speech_means[gaussian] = smk2; // Q7. + + // (Q7 >> 3) = Q4. With rounding. + tmp_s16 = ((smk + 4) >> 3); + + tmp_s16 = features[channel] - tmp_s16; // Q4 + // (Q11 * Q4 >> 3) = Q12. + tmp1_s32 = (deltaS[gaussian] * tmp_s16) >> 3; + tmp2_s32 = tmp1_s32 - 4096; + tmp_s16 = sgprvec[gaussian] >> 2; + // (Q14 >> 2) * Q12 = Q24. + tmp1_s32 = tmp_s16 * tmp2_s32; + + tmp2_s32 = tmp1_s32 >> 4; // Q20 + + // 0.1 * Q20 / Q7 = Q13. + if (tmp2_s32 > 0) { + tmp_s16 = (int16_t) WebRtcSpl_DivW32W16(tmp2_s32, ssk * 10); + } else { + tmp_s16 = (int16_t) WebRtcSpl_DivW32W16(-tmp2_s32, ssk * 10); + tmp_s16 = -tmp_s16; + } + // Divide by 4 giving an update factor of 0.025 (= 0.1 / 4). + // Note that division by 4 equals shift by 2, hence, + // (Q13 >> 8) = (Q13 >> 6) / 4 = Q7. + tmp_s16 += 128; // Rounding. + ssk += (tmp_s16 >> 8); + if (ssk < kMinStd) { + ssk = kMinStd; + } + self->speech_stds[gaussian] = ssk; + } else { + // Update GMM variance vectors. + // deltaN * (features[channel] - nmk) - 1 + // Q4 - (Q7 >> 3) = Q4. + tmp_s16 = features[channel] - (nmk >> 3); + // (Q11 * Q4 >> 3) = Q12. + tmp1_s32 = (deltaN[gaussian] * tmp_s16) >> 3; + tmp1_s32 -= 4096; + + // (Q14 >> 2) * Q12 = Q24. + tmp_s16 = (ngprvec[gaussian] + 2) >> 2; + tmp2_s32 = OverflowingMulS16ByS32ToS32(tmp_s16, tmp1_s32); + // Q20 * approx 0.001 (2^-10=0.0009766), hence, + // (Q24 >> 14) = (Q24 >> 4) / 2^10 = Q20. + tmp1_s32 = tmp2_s32 >> 14; + + // Q20 / Q7 = Q13. + if (tmp1_s32 > 0) { + tmp_s16 = (int16_t) WebRtcSpl_DivW32W16(tmp1_s32, nsk); + } else { + tmp_s16 = (int16_t) WebRtcSpl_DivW32W16(-tmp1_s32, nsk); + tmp_s16 = -tmp_s16; + } + tmp_s16 += 32; // Rounding + nsk += tmp_s16 >> 6; // Q13 >> 6 = Q7. + if (nsk < kMinStd) { + nsk = kMinStd; + } + self->noise_stds[gaussian] = nsk; + } + } + + // Separate models if they are too close. + // `noise_global_mean` in Q14 (= Q7 * Q7). + noise_global_mean = WeightedAverage(&self->noise_means[channel], 0, + &kNoiseDataWeights[channel]); + + // `speech_global_mean` in Q14 (= Q7 * Q7). + speech_global_mean = WeightedAverage(&self->speech_means[channel], 0, + &kSpeechDataWeights[channel]); + + // `diff` = "global" speech mean - "global" noise mean. + // (Q14 >> 9) - (Q14 >> 9) = Q5. + diff = (int16_t) (speech_global_mean >> 9) - + (int16_t) (noise_global_mean >> 9); + if (diff < kMinimumDifference[channel]) { + tmp_s16 = kMinimumDifference[channel] - diff; + + // `tmp1_s16` = ~0.8 * (kMinimumDifference - diff) in Q7. + // `tmp2_s16` = ~0.2 * (kMinimumDifference - diff) in Q7. + tmp1_s16 = (int16_t)((13 * tmp_s16) >> 2); + tmp2_s16 = (int16_t)((3 * tmp_s16) >> 2); + + // Move Gaussian means for speech model by `tmp1_s16` and update + // `speech_global_mean`. Note that `self->speech_means[channel]` is + // changed after the call. + speech_global_mean = WeightedAverage(&self->speech_means[channel], + tmp1_s16, + &kSpeechDataWeights[channel]); + + // Move Gaussian means for noise model by -`tmp2_s16` and update + // `noise_global_mean`. Note that `self->noise_means[channel]` is + // changed after the call. + noise_global_mean = WeightedAverage(&self->noise_means[channel], + -tmp2_s16, + &kNoiseDataWeights[channel]); + } + + // Control that the speech & noise means do not drift to much. + maxspe = kMaximumSpeech[channel]; + tmp2_s16 = (int16_t) (speech_global_mean >> 7); + if (tmp2_s16 > maxspe) { + // Upper limit of speech model. + tmp2_s16 -= maxspe; + + for (k = 0; k < kNumGaussians; k++) { + self->speech_means[channel + k * kNumChannels] -= tmp2_s16; + } + } + + tmp2_s16 = (int16_t) (noise_global_mean >> 7); + if (tmp2_s16 > kMaximumNoise[channel]) { + tmp2_s16 -= kMaximumNoise[channel]; + + for (k = 0; k < kNumGaussians; k++) { + self->noise_means[channel + k * kNumChannels] -= tmp2_s16; + } + } + } + self->frame_counter++; + } + + // Smooth with respect to transition hysteresis. + if (!vadflag) { + if (self->over_hang > 0) { + vadflag = 2 + self->over_hang; + self->over_hang--; + } + self->num_of_speech = 0; + } else { + self->num_of_speech++; + if (self->num_of_speech > kMaxSpeechFrames) { + self->num_of_speech = kMaxSpeechFrames; + self->over_hang = overhead2; + } else { + self->over_hang = overhead1; + } + } + return vadflag; +} + +// Initialize the VAD. Set aggressiveness mode to default value. +int WebRtcVad_InitCore(VadInstT* self) { + int i; + + if (self == NULL) { + return -1; + } + + // Initialization of general struct variables. + self->vad = 1; // Speech active (=1). + self->frame_counter = 0; + self->over_hang = 0; + self->num_of_speech = 0; + + // Initialization of downsampling filter state. + memset(self->downsampling_filter_states, 0, + sizeof(self->downsampling_filter_states)); + + // Initialization of 48 to 8 kHz downsampling. + WebRtcSpl_ResetResample48khzTo8khz(&self->state_48_to_8); + + // Read initial PDF parameters. + for (i = 0; i < kTableSize; i++) { + self->noise_means[i] = kNoiseDataMeans[i]; + self->speech_means[i] = kSpeechDataMeans[i]; + self->noise_stds[i] = kNoiseDataStds[i]; + self->speech_stds[i] = kSpeechDataStds[i]; + } + + // Initialize Index and Minimum value vectors. + for (i = 0; i < 16 * kNumChannels; i++) { + self->low_value_vector[i] = 10000; + self->index_vector[i] = 0; + } + + // Initialize splitting filter states. + memset(self->upper_state, 0, sizeof(self->upper_state)); + memset(self->lower_state, 0, sizeof(self->lower_state)); + + // Initialize high pass filter states. + memset(self->hp_filter_state, 0, sizeof(self->hp_filter_state)); + + // Initialize mean value memory, for WebRtcVad_FindMinimum(). + for (i = 0; i < kNumChannels; i++) { + self->mean_value[i] = 1600; + } + + // Set aggressiveness mode to default (=`kDefaultMode`). + if (WebRtcVad_set_mode_core(self, kDefaultMode) != 0) { + return -1; + } + + self->init_flag = kInitCheck; + + return 0; +} + +// Set aggressiveness mode +int WebRtcVad_set_mode_core(VadInstT* self, int mode) { + int return_value = 0; + + switch (mode) { + case 0: + // Quality mode. + memcpy(self->over_hang_max_1, kOverHangMax1Q, + sizeof(self->over_hang_max_1)); + memcpy(self->over_hang_max_2, kOverHangMax2Q, + sizeof(self->over_hang_max_2)); + memcpy(self->individual, kLocalThresholdQ, + sizeof(self->individual)); + memcpy(self->total, kGlobalThresholdQ, + sizeof(self->total)); + break; + case 1: + // Low bitrate mode. + memcpy(self->over_hang_max_1, kOverHangMax1LBR, + sizeof(self->over_hang_max_1)); + memcpy(self->over_hang_max_2, kOverHangMax2LBR, + sizeof(self->over_hang_max_2)); + memcpy(self->individual, kLocalThresholdLBR, + sizeof(self->individual)); + memcpy(self->total, kGlobalThresholdLBR, + sizeof(self->total)); + break; + case 2: + // Aggressive mode. + memcpy(self->over_hang_max_1, kOverHangMax1AGG, + sizeof(self->over_hang_max_1)); + memcpy(self->over_hang_max_2, kOverHangMax2AGG, + sizeof(self->over_hang_max_2)); + memcpy(self->individual, kLocalThresholdAGG, + sizeof(self->individual)); + memcpy(self->total, kGlobalThresholdAGG, + sizeof(self->total)); + break; + case 3: + // Very aggressive mode. + memcpy(self->over_hang_max_1, kOverHangMax1VAG, + sizeof(self->over_hang_max_1)); + memcpy(self->over_hang_max_2, kOverHangMax2VAG, + sizeof(self->over_hang_max_2)); + memcpy(self->individual, kLocalThresholdVAG, + sizeof(self->individual)); + memcpy(self->total, kGlobalThresholdVAG, + sizeof(self->total)); + break; + default: + return_value = -1; + break; + } + + return return_value; +} + +// Calculate VAD decision by first extracting feature values and then calculate +// probability for both speech and background noise. + +int WebRtcVad_CalcVad48khz(VadInstT* inst, const int16_t* speech_frame, + size_t frame_length) { + int vad; + size_t i; + int16_t speech_nb[240]; // 30 ms in 8 kHz. + // `tmp_mem` is a temporary memory used by resample function, length is + // frame length in 10 ms (480 samples) + 256 extra. + int32_t tmp_mem[480 + 256] = { 0 }; + const size_t kFrameLen10ms48khz = 480; + const size_t kFrameLen10ms8khz = 80; + size_t num_10ms_frames = frame_length / kFrameLen10ms48khz; + + for (i = 0; i < num_10ms_frames; i++) { + WebRtcSpl_Resample48khzTo8khz(speech_frame, + &speech_nb[i * kFrameLen10ms8khz], + &inst->state_48_to_8, + tmp_mem); + } + + // Do VAD on an 8 kHz signal + vad = WebRtcVad_CalcVad8khz(inst, speech_nb, frame_length / 6); + + return vad; +} + +int WebRtcVad_CalcVad32khz(VadInstT* inst, const int16_t* speech_frame, + size_t frame_length) +{ + size_t len; + int vad; + int16_t speechWB[480]; // Downsampled speech frame: 960 samples (30ms in SWB) + int16_t speechNB[240]; // Downsampled speech frame: 480 samples (30ms in WB) + + + // Downsample signal 32->16->8 before doing VAD + WebRtcVad_Downsampling(speech_frame, speechWB, &(inst->downsampling_filter_states[2]), + frame_length); + len = frame_length / 2; + + WebRtcVad_Downsampling(speechWB, speechNB, inst->downsampling_filter_states, len); + len /= 2; + + // Do VAD on an 8 kHz signal + vad = WebRtcVad_CalcVad8khz(inst, speechNB, len); + + return vad; +} + +int WebRtcVad_CalcVad16khz(VadInstT* inst, const int16_t* speech_frame, + size_t frame_length) +{ + size_t len; + int vad; + int16_t speechNB[240]; // Downsampled speech frame: 480 samples (30ms in WB) + + // Wideband: Downsample signal before doing VAD + WebRtcVad_Downsampling(speech_frame, speechNB, inst->downsampling_filter_states, + frame_length); + + len = frame_length / 2; + vad = WebRtcVad_CalcVad8khz(inst, speechNB, len); + + return vad; +} + +int WebRtcVad_CalcVad8khz(VadInstT* inst, const int16_t* speech_frame, + size_t frame_length) +{ + int16_t feature_vector[kNumChannels], total_power; + + // Get power in the bands + total_power = WebRtcVad_CalculateFeatures(inst, speech_frame, frame_length, + feature_vector); + + // Make a VAD + inst->vad = GmmProbability(inst, feature_vector, total_power, frame_length); + + return inst->vad; +} |